A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Visualize the blast radius of your git branch as an interactive force-directed graph.

Shows which files changed, which files import them (affected), and how they cluster by project — all in a single HTML page with D3.js.

In this tutorial, we will install and use a full GNOME Desktop environment in WSL2, without any external software. The only requirement is a working WSLg installation. At the moment, the instructions are only for Ubuntu (20.04, 22.04 and 24.04) distros and GNOME, but you can request me to test other distros and desktop environments.

Let's do a brainstorming session for **add your intent here**
We need to discuss all aspects (functionality, UI/UX, schema, technologies, etc.)
You ask me questions and show options with [ ]. Preselectt he recommended answer.
We can do multiple rounds, but only one round at a time. 
In each round ask one or multiple questions. 
Ask me one round of questions, I answer, then we repeat until it’s done.
Use visualization when useful.